Multisource Smart Computer-Aided System for Mining COVID-19 Infection Data

Abstract

In this paper, we approach the problem of detecting and diagnosing COVID-19 infections using multisource scan images including CT and X-ray scans to assist the healthcare system during the COVID-19 pandemic. Here, a computer-aided diagnosis (CAD) system is proposed that utilizes analysis of the CT or X-ray to diagnose the impact of damage in the respiratory system per infected case. The CAD was utilized and optimized by hyper-parameters for shallow learning, e.g., SVM and deep learning. For the deep learning, mini-batch stochastic gradient descent was used to overcome fitting problems during transfer learning. The optimal parameter list values were found using the naïve Bayes technique. Our contributions are (i) a comparison among the detection rates of pre-trained CNN models, (ii) a suggested hybrid deep learning with shallow machine learning, (iii) an extensive analysis of the results of COVID-19 transition and informative conclusions through developing various transfer techniques, and (iv) a comparison of the accuracy of the previous models with the systems of the present study. The effectiveness of the proposed CAD is demonstrated using three datasets, either using an intense learning model as a fully end-to-end solution or using a hybrid deep learning model. Six experiments were designed to illustrate the superior performance of our suggested CAD when compared to other similar approaches. Our system achieves 99.94, 99.6, 100, 97.41, 99.23, and 98.94 accuracy for binary and three-class labels for the CT and two CXR datasets.

A Novel Meta-Heuristic Optimization Algorithm in White Blood Cells燙lassification

“…Due to its relative simplicity and flexibility in handling multiple classification issues, SVM provides balanced predicted performance, even in studies with limited sample sets [22]. The SVM classifier is based on the concept of the most appropriate hyper-planes employed to distinguish among classes [15,23]. Due to the goals of SVM, the decision boundary might have to be very close to one specific class to correctly label all data points in the training set [23].…”

“…The SVM classifier is based on the concept of the most appropriate hyper-planes employed to distinguish among classes [15,23]. Due to the goals of SVM, the decision boundary might have to be very close to one specific class to correctly label all data points in the training set [23]. SVM tries to minimize the number of misclassified examples due to the high penalty added by the c-parameter, meaning the results in a decision boundary would be a smaller margin.…”

“…Convolutional Neural Network (CNN) comes from deep learning. It consists mainly of convolutional, pooling, filters, fully connected, and soft-max layers [23]. CNN specializes in dealing with images due to its ability to extract images' features.…”

“…Shallow classifiers depend on the quality of the feature extraction method and the derived features map in terms of the high dimensionality of the images, which leads to complex models that consume resources and time. Convolutional layers in deep learning solve this problem by reducing the dimension of the image and extracting the desired features [23].…”